Apparatus and method for estimating bio-information

ABSTRACT

An apparatus for estimating bio-information may include: a bio-signal acquirer configured to acquire a bio-signal; and a processor configured to extract one or more first feature values from the bio-signal, determine a scale factor based on the first feature values, and to estimate bio-information based on the scale factor and the first feature values.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application claims priority from Korean Patent Application No.10-2018-0019441, filed on Feb. 19, 2018, in the Korean IntellectualProperty Office and Korean Patent Application No. 10-2019-0002484, filedon Jan. 8, 2019, in the Korean Intellectual Property Office, thedisclosures of which are incorporated herein by reference in theirentireties.

BACKGROUND 1. Field

Apparatuses and methods consistent with example embodiments relate tonon-invasively estimating bio-information using bio-signals.

2. Description of the Related Art

Recently, with the aging population, soaring medical costs, and ashortage of medical personnel for specialized medical services, researchis being actively conducted on tech convergence for medical devices.

Particularly, monitoring of the health condition of the human body isnot limited to medical institutions, but is expanding to mobilehealthcare fields that may monitor a user's health condition anywhereand anytime in daily life at home or office.

Typical examples of bio-signals, which indicate the health condition ofindividuals, include an electrocardiography (ECG) signal, aphotoplethysmogram (PPG) signal, an electromyography (EMG) signal, andthe like, and various bio-signal sensors have been developed to measurethese signals in daily life.

For example, according to studies on the PPG signal, the entire PPGsignal is a superposition of propagation waves starting from the hearttoward the distal end portions of the body and reflection wavesreturning back from the distal end portions.

Further, it has been known that information for estimating bloodpressure may be obtained by extracting various features associated withthe propagation waves or the reflection waves.

In some cases, however, the method of estimating bio-information frombio-signals to monitor health conditions in daily life may provide anunstable estimation result of bio-information due to deterioration inbio-signal quality, interference of motion noise, and the like, andvarious studies have been conducted to solve the problem.

SUMMARY

Example embodiments address at least the above problems and/ordisadvantages and other disadvantages not described above. Also, theexample embodiments are not required to overcome the disadvantagesdescribed above, and may not overcome any of the problems describedabove.

One or more example embodiments provide an apparatus and a method forestimating bio-information, in which scale conversion is performed onfeatures extracted from a bio-signal, and therefore bio-information maybe estimated accurately and stably even when the bio-signal is measuredin an unstable environment.

According to an aspect of an example embodiment, there is provided anapparatus for estimating bio-information, the apparatus including: abio-signal acquirer configured to acquire a bio-signal; and a processorconfigured to extract one or more first feature values from thebio-signal, determine a scale factor based on the first feature values,and estimate bio-information based on the scale factor and the firstfeature values.

The first features may include a feature associated with cardiac output(CO), a feature associated with total peripheral resistance (TPR), and acombination of the feature associated with CO and the feature associatedwith the TPR.

The processor may calculate a second feature value by combining thefirst feature values, may calculate based on the second feature value,and may adjust a reference scale factor based on the scale control ratioto determine the scale factor.

Further, the processor may calculate the second feature value bycombining at least one of an individual variation and a combinedvariation of the first feature values.

The processor may calculate the scale control ratio according to amagnitude of the second feature value by applying the second featurevalue to a scale control ratio decision function.

Here, the scale control ratio decision function may be expressed as agraph having a valley shape, in which the scale control ratio has aminimum value at a point of a reference second feature value andincreases with a change in the second feature value from the referencesecond feature value, and the scale control ratio is saturated to apredetermined scale control ratio in an area of the graph where thesecond feature value falls outside a threshold range.

The processor may calculate individual scale control ratios for thefirst feature values, may calculate a scale control ratio based on astatistical value of the individual scale control ratios, and maydetermine the scale factor based on the scale control ratio.

In addition, the processor may calculate a third feature value bycombining the first feature values, and may estimate bio-informationbased on the third feature value and the scale factor.

Moreover, the processor may estimate the bio-information by multiplyinga difference between the third feature value and a reference thirdfeature value by the scale factor, and adding an offset value to themultiplied difference.

In response to the first feature value exceeding a predeterminedthreshold value, the processor may determine a reference scale factor tobe the scale factor.

The processor may normalize the first feature values based on areference first feature value.

The apparatus for estimating bio-information may further including anoutput interface configured to output the bio-signal, feature values ofthe bio-signal, a multiplication coefficient ratio control factor, amultiplication coefficient ratio, and the bio-information.

The bio-information may include blood pressure, pulse, cardiac output,blood glucose, triglycerides, and keratin.

According to an aspect of another example embodiment, there is provideda method of estimating bio-information, the method including: acquiringa bio-signal; extracting one or more first feature values from thebio-signal; determining a scale factor based on the first featurevalues; and estimating bio-information based on the scale factor and thefirst feature values.

The determining of the scale factor may include: calculating a secondfeature value by combining the first feature values; calculating a scalecontrol ratio based on the second feature value; and determining thescale factor by adjusting a reference scale factor based on the scalecontrol ratio.

Further, the calculating of the second feature value may includecombining at least one of an individual variation and a combinedvariation of the first feature values.

In this case, the calculating of the scale control ratio may includecalculating the scale control ratio according to a magnitude of thesecond feature value by applying the calculated second feature value toa scale control ratio decision function.

In addition, the scale control ratio decision function may be expressedas a graph having a valley shape, in which the scale control ratio has aminimum value at a point of a reference second feature value andincreases with a change in the second feature value from the referencesecond feature value, and the scale control ratio is saturated to apredetermined scale control ratio in an area of the graph where thesecond feature value falls outside a threshold range.

In this case, the determining of the scale factor may include:calculating individual scale control ratios for the first featurevalues; calculating a scale control ratio based on a statistical valueof the individual scale control ratios; and determining the scale factorby adjusting the reference scale factor based on the calculated scalecontrol ratio.

In addition, the estimating of the bio-information may include:calculating a third feature value by combining the first feature values;and multiplying a difference between the third feature value and areference third feature value by the scale factor, and adding an offsetvalue to the multiplied difference to estimate the bio-information.

The determining of the scale factor may include: determining whether thefirst feature value exceeds a predetermined threshold value; and upondetermining that the first feature value exceeds the predeterminedthreshold value, determining a reference scale factor to be the scalefactor.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and/or other aspects will be more apparent by describingcertain example embodiments, with reference to the accompanyingdrawings, in which:

FIG. 1 is a block diagram illustrating an apparatus for estimatingbio-information according to an example embodiment;

FIG. 2 is a diagram illustrating a bio-signal according to an exampleembodiment;

FIG. 3 is a diagram explaining an example of adjusting a scale factoraccording to an example embodiment;

FIG. 4 is a diagram explaining an example of calculating a secondfeature value according to an example embodiment;

FIG. 5 is a diagram illustrating an example of calculating a scalecontrol ratio according to an example embodiment;

FIG. 6 is a diagram explaining another example of calculating a scalecontrol ratio according to an example embodiment;

FIG. 7 is a block diagram illustrating an apparatus for estimatingbio-information according to another example embodiment;

FIG. 8 is a flowchart illustrating a method of estimatingbio-information according to an example embodiment; and

FIG. 9 is a flowchart illustrating a method of estimatingbio-information according to another example embodiment.

DETAILED DESCRIPTION

Example embodiments are described in greater detail below with referenceto the accompanying drawings.

In the following description, like drawing reference numerals are usedfor like elements, even in different drawings. The matters defined inthe description, such as detailed construction and elements, areprovided to assist in a comprehensive understanding of the exampleembodiments. However, it is apparent that the example embodiments can bepracticed without those specifically defined matters. Also, well-knownfunctions or constructions are not described in detail since they wouldobscure the description with unnecessary detail.

Process steps described herein may be performed differently from aspecified order, unless a specified order is clearly stated in thecontext of the disclosure. That is, each step may be performed in aspecified order, at substantially the same time, or in a reverse order.

Further, the terms used throughout this specification are defined inconsideration of the functions according to example embodiments, and canbe varied according to a purpose of a user or manager, or precedent andso on. Therefore, definitions of the terms should be made on the basisof the overall context.

It will be understood that, although the terms first, second, etc. maybe used herein to describe various elements, these elements should notbe limited by these terms. These terms are only used to distinguish oneelement from another. Any references to singular may include pluralunless expressly stated otherwise. In the present specification, itshould be understood that the terms, such as ‘including’ or ‘having.’etc., are intended to indicate the existence of the features, numbers,steps, actions, components, parts. or combinations thereof disclosed inthe specification, and are not intended to preclude the possibility thatone or more other features, numbers, steps, actions, components, parts,or combinations thereof may exist or may be added.

Expressions such as “at least one of,” when preceding a list ofelements, modify the entire list of elements and do not modify theindividual elements of the list. For example, the expression, “at leastone of a, b, and c,” should be understood as including only a, only b,only c, both a and b, both a and c, both b and c, all of a, b, and c, orany variations of the aforementioned examples.

FIG. 1 is a block diagram illustrating an apparatus for estimatingbio-information according to an example embodiment, and FIG. 2 is adiagram illustrating a bio-signal according to an example embodiment.

The bio-information estimating apparatus 100 may acquire a bio-signal,may extract features from the acquired bio-signal, and may estimatebio-information based on the extracted features.

For example, the bio-information estimating apparatus 100 may extract afirst feature from a photoplethysmography (PPG) signal which is composedof a superposition of propagation waves and reflection waves illustratedin FIG. 2, and may estimate blood pressure by multiplying the firstfeature by a scale factor, and by adding an offset value, such as ablood pressure value in a stable state, to the multiplied first feature.Here, the first feature may include a feature f_(1_co) associated withcardiac output (CO), a feature f_(1_TPR) associated with totalperipheral resistance (TPR), a combination thereof, and the like, inwhich the combination may include addition, subtraction, multiplication,division, and the like of the feature f_(1_co) associated with cardiacoutput (CO) and the feature f_(1_TPR) associated with total peripheralresistance (TPR), and may include further adding or subtracting a realnumber to and from the added, subtracted, multiplied, divided value, andthe like. Cardiac output (CO) is the amount of blood the heart pumps outover a unit of time. The total peripheral resistance (TPR) is a totalresistance offered by systemic arteries to the blood flow across thesystemic arteries. For example, the feature f_(1_co) associated withcardiac output (CO) and the feature f_(1_TPR) associated with totalperipheral resistance (TPR) may be obtained by extracting at least onefeature point in a PPG signal (e.g., a peak point of the PPG signal, apeak point of each of the propagation wave and the reflection waves),extracting time and/or an amplitude of the at least one feature pointand linearly or non-linearly combining the time and/or the amplitude ofthe at least one feature point, but the example embodiment is notlimited therefore. Doppler ultrasound, thoracic bioimpedance, pulsecontour analysis, or multi-linear regression analysis based on a pulsewidth of a PPG signal may be used to extract the feature f_(1_co)associated with cardiac output (CO) and feature f_(1_TPR) associatedwith total peripheral resistance (TPR).

In this case, the bio-information estimating apparatus 100 may stablyestimate bio-information by calculating an adaptive scale factor basedon a variation of the extracted first features.

The bio-information estimation apparatus 100 may calculate a scalecontrol ratio for adjusting a scale factor based on the extracted firstfeature, and may determine a scale factor adaptively to the variation ofthe first feature values by adjusting a reference scale factor based onthe calculated scale control ratio. For example, the reference scalefactor may have a predetermined value, and the scale control ratio mayhave a value that is higher than 0 and less than or equal to 1. If theextracted first feature has a value greater than a predeterminedthreshold, the scale control ratio may have a value of 1 so that thebio-information is estimated using the reference scale factor withoutadjustment of the reference scale factor. If the extracted first featurehas a value less than or equal to the predetermined threshold, the scalecontrol ratio may have a value between 0 and 1 and the reference scalefactor may be scaled down according to the value of the scale controlratio.

As described above, by adaptively determining the scale factor, thebio-information estimating apparatus 100 may stably estimatebio-information even when a bio-signal is estimated in a poorenvironment, such as in the case where an unstable bio-signal isacquired due to motion noise and the like.

FIG. 3 is a diagram explaining an example of adjusting a scale factoraccording to an example embodiment.

Referring to FIG. 3, depending on whether a variation of features (e.g.,features f_(1_co) and f_(1_TPR)) extracted from a bio-signal belongs toa homoeostasis maintaining region, a linear change region, or anon-linear saturation region, a changing shape of bio-information mayvary with respect to a change in the features extracted from thebio-signal.

For example, in the homoeostasis maintaining region, a variation ofbio-information is smaller than a change in features of the bio-signalaccording to human body characteristics of maintaining homoeostasis; inthe linear change region, the variation of bio-information has apredetermined correlation with the change in features of the bio-signal;and in the non-linear saturation region, bio-information changesirregularly having a non-linear correlation or no specific correlationwith the change in features of the bio-signal.

Accordingly, the bio-information estimating apparatus 100 may determinewhether the first feature value extracted from the bio-signal or avariation in the first feature value belongs to the homoeostasismaintaining region, the linear change region, or the non-linearsaturation region, and may estimate bio-information stably andaccurately by adjusting a scale factor based on the determination.

More specifically, the bio-information estimating apparatus 100 maydetermine that the first feature value belongs to the homoeostasismaintaining region when the variation in the first feature value isgreater than the variation in the bio-signal, may determine that thefirst feature value belongs to the linear change region when thecorrelation between the variation in the first feature and the variationin the bio-signal is greater than equal to a predetermined correlationvalue, and may determine that the first feature value belongs to thenon-linear saturation region when the correlation between the variationin the first feature and the variation in the bio-signal is less thanthe predetermined correlation value.

For example, in the case where the first feature value belongs to thehomoeostasis maintaining region, the bio-information estimatingapparatus 100 may decrease a scale factor by considering homoeostasismaintaining characteristics, and thus may reduce an effect of the changein the first feature value on the change in bio-information.

In another example, in the case where the first feature value belongs tothe linear change region, the bio-information estimating apparatus 100may determine a predetermined reference scale factor to be a scalefactor, such that the change in the first feature value may be reflectedas it is in the bio-information.

In yet another example, in the case where the first feature valuebelongs to the non-linear saturation region, the bio-informationestimating apparatus 100 may adjust a scale factor by applying the firstfeature value to a bio-information estimation model, which ispre-generated by a non-linear function or machine learning, so that thechange in the first feature value may be reflected in the change ofbio-information.

As described above, by adjusting a scale factor according to the changein the features extracted from the bio-signal, the bio-informationestimating apparatus 100 may accept a feature change as it is, which mayaffect estimation of bio-information, and may reflect the feature changein estimation of bio-information. Further, the bio-informationestimating apparatus 100 may adaptively decrease a feature change, whichdoes not affect estimation of bio-information, and may reduce errorcaused by unnecessary motion noise. Accordingly, the bio-informationestimating apparatus 100 may stably estimate bio-information even in anenvironment where an unstable bio-signal is measured.

For convenience of explanation, the following description will be madeusing an example of estimating blood pressure based on a bio-signal.However, bio-information is not limited thereto, and may include bloodpressure, pulse, oxygen saturation, stress index, blood glucose,triglycerides, keratin, and the like.

Referring back to FIG. 1, an example of estimating bio-information bythe bio-information estimating apparatus 100 will be described in detailbelow.

As illustrated in FIG. 1, the bio-information estimating apparatus 100includes a bio-signal acquirer 110 and a processor 120. Here, theprocessor 120 may be composed of one or more processors, a memory, and acombination thereof.

The bio-signal acquirer 110 may acquire a bio-signal of a user.

Here, the bio-signal may include an electrocardiogram (ECG) signal, aphotoplethysmography (PPG) signal, an electromyography (EMG) signal, aballistocardiogram (BCG) signal, a cardiac output (CO) signal, a Totalperipheral resistance (TPR) signal, heart sound, and the like.

For example, the bio-signal acquirer 110 may include a sensor includingat least one of the following: one or more electrodes for measuring abio-signal, a PPG sensor, an ECG sensor, a pressure sensor, and aphotodetector module including a light source and a detector. Thebio-signal acquirer 110 may directly interface with a user through thesensor to acquire a bio-signal.

Further, the bio-signal acquirer 110 may include a communicationinterface to communicate with an external device to receive bio-signaldata of a user from the external device. For example, the bio-signalacquirer 110 may receive bio-signal data of a user from the externaldevice using Bluetooth communication, Bluetooth Low Energy (BLE)communication, Near Field Communication (NFC), WLAN communication,Zigbee communication, Infrared Data Association (IrDA) communication,Wi-Fi Direct (WFD) communication, Ultra-Wideband (UWB) communication,Ant+ communication, WIFI communication, Radio Frequency Identification(RFID) communication, and the like. In addition, examples of theexternal device may include a cellular phone, a smartphone, a tablet PC,a laptop computer, a personal digital assistant (PDA), a portablemultimedia player (PMP), a navigation, an MP3 player, a digital camera,a wearable device, and the like. However, the external device is notlimited to the above examples, and may include various devices whichstore bio-signal data of a user.

The processor 120 may extract one or more first feature values f₁ fromthe acquired bio-signal.

The first feature values, which are extracted from the bio-signal, mayindicate features having a predetermined correlation withbio-information desired to be estimated. One or more first featurevalues may be extracted; and in the case of estimating blood pressure byusing, for example, the bio-information estimating apparatus 100, thefirst features may include a feature f_(1_co) associated with cardiacoutput (CO) which indicates the blood volume pumped by the heart in oneminute, a feature f_(1_TPR) associated with total peripheral resistance(TPR), a combination thereof, and the like. The first features may varydepending on the types of bio-information desired to be estimated.

Upon extracting the first feature values from the bio-signal, theprocessor 120 may convert the first feature values.

For example, the processor 120 may normalize the extracted first featurevalues by dividing the first feature value (e.g.,f_(1_co_norm)=f_(1_co)/f_(1_co_ref),f_(1_TPR_norm)=f_(1_TPR)/f_(1_TPR_ref), etc.), extracted from thebio-signal, by a reference first feature value extracted in a referencestate (e.g., f_(1_co _ref), f_(1_TPR_ref), etc.).

In this case, the reference state is a resting state except for a sleepstate, and may refer to, for example, a state in which pulse andrespiration rates are stable or a state in which blood pressure measuredby an external device for measuring blood pressure is maintained withoutsubstantial change. The reference state may be measured from a testsubject while the test subject is awake and resting without exercising.

Further, a reference second feature value and a reference third value,which will be described later, may refer to a second feature value and athird feature value which are calculated based on the reference firstfeature value extracted in the reference state. Hereinafter, the firstfeature value may refer to the first feature value normalized using thereference first feature value.

The processor 120 may determine a scale factor based on the extractedfirst feature values.

The scale factor may be a coefficient for adjusting a scale of the firstfeature value extracted for estimating bio-information, but is notlimited thereto, and may be a coefficient for adjusting a scale of thethird feature value calculated based on the first feature value forestimating bio-information, as will be described later.

For example, the processor 120 may calculate a second feature valuef_(sc) by combining the first feature values, and may calculate a scalecontrol ratio based on the second feature value.

Here, the second feature value may indicate a feature value fordetermining the scale control ratio, and the processor 120 may calculatethe second feature value using an individual variation or a combinedvariation of first feature values.

FIG. 4 is a diagram explaining an example of calculating a secondfeature value according to an example embodiment.

Referring to FIGS. 1 and 4, FIG. 4 illustrates a variation in firstfeature values f_(1a) and f_(1b) according to elapsed time. Inparticular, the processor 120 may calculate the variation in the firstfeature values based on a reference second feature value f_(sc,ref).

Upon calculating one or more first feature values, the processor 120 maycalculate a second feature value by using an individual variation and/ora combined variation of the first feature values.

In this case, the combined variation may refer to a difference between avalue, obtained by linear combination of the first feature values f_(1a)and f_(1b) (e.g., addition, subtraction, and multiplication of f_(1a)and f_(1b), a combination thereof, etc.), and the reference secondfeature value f_(sc,ref); and the individual variation may refer to adifference between each of the first feature values f_(1a) and f_(1b)and the reference second feature value f_(sc,ref).

For example, the following Equation 1 may represent an example ofcalculating the second feature value f_(sc) by using the combinedvariation of the first feature values f_(1a) and f_(1b).

f _(sc) =f _(1a) +f _(1b) −f _(sc,ref)  [Equation 1]

Further, the following Equation 2 may represent another example ofcalculating the second feature value f_(sc) by using the individualvariation of the first feature values f_(1a) and f_(1b).

f _(sc)=(f _(1a) −f _(1b) +|f _(1a) −f _(sc,ref) |+|f _(1b) −f_(sc,ref)|)/2  [Equation 2]

In addition, the following Equation 3 may represent yet another exampleof calculating the second feature f_(sc) by using a combination of thecombined variation and the individual variation of the first featurevalues f_(1a) and f_(1b).

f _(sc)=(f _(1a) +f _(1b) +|f _(1a) −f _(sc,ref) |+|f _(1b) −f_(sc,ref)|)/2  [Equation 3]

As described above, the processor 120 may calculate the second featurevalue f_(sc) for determining a scale control ratio by using individualfirst feature values f_(1a) and f_(1b) and/or a combination thereof.

In another example, the processor 120 may calculate the second featurevalue f_(sc) by applying a weight to a first feature value, having ahigher correlation with bio-information to be estimated than other firstfeature values among the extracted first feature values, which isrepresented by the following Equation 4.

f _(sc) =α*f _(1a) +β*f _(1b) −f _(sc,ref)  [Equation 4]

α and β may denote weights that are respectively applied to the firstfeature values f_(1a) and f_(1b). α may have a value greater than β whenthe correlation between the first feature value f_(1a) and thebio-information is greater than the correlation between the firstfeature value f_(1b) and the bio-information.

Equations 1 to 4 may represent examples of calculating the secondfeature value, but the calculation of the second feature value is notlimited thereto, and the second feature value for determining a scalecontrol ratio may be determined by various combinations of the firstfeature values.

Upon calculating the second feature value, the processor 120 maycalculate the scale control ratio based on the second feature value, andmay determine a scale factor by adjusting a reference scale factor basedon the calculated scale control ratio.

Based on a magnitude of the calculated second feature value, theprocessor 120 may determine whether a variation in the first featurevalue belongs to the homoeostasis maintaining region, the linear changeregion, or the non-linear saturation region, and may calculate a scalecontrol ratio based on the determination.

For example, in the case where the calculated second feature valuebelongs to the homoeostasis maintaining region, the processor 120 maydecrease a reference scale factor by calculating the scale control ratioto be 1 or lower, and may determine the decreased reference scale factorto be a scale factor, thereby reducing an effect of the change in thefirst feature value on the change in bio-information.

In another example, in the case where the calculated second featurevalue belongs to the linear change region, the processor 120 maydetermine a predetermined reference scale factor to be a scale factor bydetermining a scale control ratio to be 1. In this manner, by using thepredetermined reference scale factor as a scale factor, the processor120 may reflect the change in the first feature value in the change ofbio-information according to the predetermined ratio.

In yet another example, in the case where the second feature valuebelongs to the non-linear saturation region, the processor 120 maydetermine a scale control ratio for adjusting the reference scale factorby applying the second feature value to a predetermined scale controlestimation model, which is pre-generated by a non-linear function ormachine learning, so that the change in the first feature value may bereflected in the change of bio-information.

As described above, the processor 120 may adjust the reference scalefactor based on the calculated scale control ratio, and may estimatebio-information by adaptively adjusting the scale factor for estimatingbio-information.

The processor 120 may calculate the scale control ratio according to amagnitude of the second feature value by applying the second featurevalue to a scale control ratio decision function ρ_(sc).

FIG. 5 is a diagram illustrating an example of calculating a scalecontrol ratio according to an example embodiment.

Referring to FIG. 5, a scale control ratio decision functionρ_(sc)(f_(sc)) may have a valley shape, in which the scale control ratiohas a minimum value ρ_(min) at a point of the reference second featurevalue f_(sc_ref), and increases with the change in the second featurevalue from the reference second feature value, and in the case where thesecond feature value falls outside a threshold range, the scale controlratio is saturated to a predetermined scale control ratio.

Here, the threshold range may indicate an interval between a low pointμ_(low) and a high point μ_(high) of the second feature value. In otherwords, the threshold range may indicate a region where the first featurevalue is changed from the homoeostasis maintaining region, and a regionwhere the scale control ratio changes adaptively. That is, in the casewhere the second feature value f_(sc) changes within the predeterminedthreshold range, the scale control ratio increases in both directionsfrom the reference second feature value.

As described above, since the scale control ratio has a minimum valueμ_(min) at a point of the reference second feature value f_(sc_ref), thechange of bio-signal features in a stable state, such as a referencestate, may have a small effect on estimation of bio-information; and asthe second feature value deviates from the reference state, the scalecontrol ratio for estimating bio-information increases. Accordingly, asan amplitude of a bio-signal increases, the change of the bio-signalfeatures may have a greater effect on estimation of bio-information.

Then, as the second feature value f_(sc) continuously changes to falloutside the threshold range, the scale control ratio ρ_(sc)(f_(sc)) issaturated to a predetermined scale control ratio (e.g., 1), such thatthe processor 120 may determine the predetermined reference scale factoritself to be a scale factor.

Referring back to FIG. 5, the scale control ratio decision functions (a)and (b) may change linearly from the reference second feature valuewithin a threshold range. However, as can be seen from the scale controlratio decision function (b), a slope of the scale control ratio decisionfunction (a) may be determined differently according to a low pointμ_(low) and a high point ρ_(high) of the second feature value.

In addition, the scale control ratio decision function (c) may be givenin the form of a power function of degree n (e.g., quadratic function,etc.) within a threshold range; and the scale control ratio decisionfunction (d) may be given in the form of a trigonometric function (e.g.,cosine function, etc.) within a threshold range.

The shape of the graph within a threshold range of the scale controlratio decision function may vary depending on a bio-signal andbio-information to be estimated, and may be pre-generated based on anestimation model, which is pre-generated by machine learning or based ona correlation between the bio-signal and bio-information. Further, theshape of the graph is not limited thereto, and the processor 120 mayperiodically acquire bio-signals of a user, and may directly generate ascale control ratio decision function from a learning model forgenerating the scale control ratio decision function.

Further, the processor 120 may calculate individual scale control ratiosfor the first feature values, and may calculate a scale control ratiobased on a statistical value of the individual scale control ratios.

FIG. 6 is a diagram explaining an example of calculating a scale controlratio according to another example embodiment.

Referring to FIGS. 1 and 6, the processor 120 may calculate anindividual scale control ratio for each of the first feature valuesbased on the scale control ratio decision function for each of the firstfeature values.

For example, upon extracting first feature values f_(1a), f_(1b), andf_(1c), instead of generating a second feature value by combining thefirst feature values, the processor 120 may calculate individual scalecontrol ratios ρ₁, ρ₂, and ρ₃ using scale control ratio functionsρ₁(f_(1a)), ρ₂(f_(1b)), and ρ₃(f_(1c)) for each of the first featurevalues, and may use a statistical value of the individual scale controlratios as a scale control ratio.

For example, the processor 120 may calculate a mean value of theindividual scale control ratios (e.g., ρ=(ρ₁+ρ₂+ρ₃)/3) as a scalecontrol ratio; and the processor 120 may apply a weighted value tofeatures, having a higher correlation with bio-information to beestimated than other feature values among the extracted first featurevalues, and may calculate a mean value of the features (e.g.,p=(α*ρ₁+β*ρ₂+γ*ρ₃)/3) as a scale control ratio. However, the scalecontrol ratio is not limited thereto, and the processor 120 maycalculate a statistical value, such as a maximum value, a minimum value,and a median value, of the individual scale control ratios, as the scalecontrol ratio.

In addition, in response to the first feature value exceeding apredetermined threshold value, the processor 120 may determine thereference scale factor to be a scale factor.

For example, the processor 120 may determine whether the first featurevalue exceeds a predetermined threshold value.

In one embodiment, the processor 120 may compare the extracted firstfeature value with a value at a predetermined low point μ_(low) or apredetermined high point μ_(high) of the scale control ratio decisionfunction, and may determine whether the first feature value is lowerthan the predetermined low point μ_(low) or higher than thepredetermined high point μ_(high).

That is, the processor 120 may compare the first feature value with apredetermined threshold value; and in response to the first featurevalue exceeding the threshold value, the processor 120 may determinethat a variation in the first feature value deviates from thehomoeostasis maintaining region and enters the linear change region, andmay determine the reference scale factor to be a scale control factor.

Further, in response to the first feature value being lower than thepredetermined threshold value, the processor 120 may determine that avariation in the first feature value belongs to the homoeostasismaintaining region, and may calculate a scale control ratio to adjust areference scale factor based on the calculated scale control ratio.

As described above, the processor 120 may determine whether the firstfeature value exceeds a predetermined threshold value; and in responseto the first feature value exceeding the threshold value, the processor120 may omit calculation of the scale control ratio, thereby accuratelyand rapidly estimating bio-information.

The processor 120 may calculate a third feature value f_(est) bycombining the first feature values, and may estimate bio-informationbased on the calculated third feature value and the determined scalefactor.

For example, upon calculating the third feature value, the processor 120may estimate bio-information using a bio-information estimation model asrepresented by the following Equation 5.

BI _(est) =SF*(f _(est) −f _(est_ref))+BI _(offset)  [Equation 5]

Herein, BI denotes bio-information to be estimated, SF denotes a scalefactor, f_(est_ref) denotes a reference third feature value, andBI_(offset) denotes an offset value for bio-information to be estimated.BI_(offset) may refer to bio-information estimated in a reference state,and may be a reference value measured by an external device forestimating bio-information, and the value may vary according to thetypes of bio-information to be estimated.

That is, upon calculating the third feature value, the processor 120 mayestimate bio-information by multiplying a difference between the thirdfeature value and the reference third feature value by the determinedscale factor, and by adding the offset value for bio-information to themultiplied value.

In this case, the second feature value and the third feature value maybe calculated by different combinations of the first feature values.

For example, the second feature value and the third feature value arecalculated by a combination of the first feature values, but methods ofcombining the first feature values to calculate the second feature valueand the third feature value may be different from each other. However,the calculation is not limited thereto, and the second feature value andthe third feature value may be calculated by the same combination of thefirst feature values.

As described above, the bio-information estimating apparatus 100 mayadaptively change the reference scale factor by using the individualscale control ratio for each of the first feature values and/or thescale control ratio calculated based on the second feature value, andthus may stably estimate bio-information.

FIG. 7 is a block diagram illustrating another example of an apparatusfor estimating bio-information.

Referring to FIG. 7, the bio-information estimating apparatus 700includes a bio-signal acquirer 710, a processor 720, an input interface730, a memory 740, a communication interface 750, and an outputinterface 760. Here, the bio-signal acquirer 710 and the processor 720perform substantially the same function as the bio-signal acquirer 110and the processor 120 described above with reference to FIG. 1, suchthat the following description will be made based on non-overlappingparts.

The input interface 730 may receive input of various operation signalsand data required for estimating bio-information from a user.

For example, the input interface 730 may include a keypad, a domeswitch, a touch pad (static pressure/capacitance), a jog wheel, a jogswitch, a hardware (H/W) button, and the like. Particularly, the touchpad, which forms a layer structure with a display, may be called a touchscreen.

For example, the input interface 730 may receive user featureinformation including one or more of age, gender, weight, body massindex (BMI), and disease history of users, or a measurement point of abio-signal, types of a bio-signal and bio-information, and the like.

The memory 740 may store programs or commands for operation of thebio-information estimating apparatus 700, and may store data input toand output from the bio-information estimating apparatus 700. Forexample, the memory 740 may store the user information input through theinput interface 730, the bio-signal data acquired by the bio-signalacquirer 710, the extracted first feature values, the calculated secondfeature value and third feature value, the scale factor, the referencescale factor, the scale control ratio, individual scale control ratios,the scale control ratio decision function, the reference first featurevalue, the reference second feature value, the reference third featurevalue, and the bio-information estimation model.

The memory 740 may include at least one storage medium of a flash memorytype memory, a hard disk type memory, a multimedia card micro typememory, a card type memory (e.g., an SD memory, an XD memory, etc.), aRandom Access Memory (RAM), a Static Random Access Memory (SRAM), a ReadOnly Memory (ROM), an Electrically Erasable Programmable Read OnlyMemory (EEPROM), a Programmable Read Only Memory (PROM), a magneticmemory, a magnetic disk, and an optical disk, and the like. Further, thebio-information estimating apparatus 700 may operate an external storagemedium, such as web storage and the like, which performs a storagefunction of the memory 740 on the Internet.

The communication interface 750 may perform communication with anexternal device. For example, the communication interface 750 maytransmit, to the external device, user feature information input throughthe input interface 730, the bio-signal acquired by the bio-signalacquirer 710, an estimation result of bio-information of the processor720, and the like; or may receive, from the external device, variousdata such as user feature information, the bio-signal, the scale controlratio decision function, the bio-information estimation model, and thelike.

In this case, the external device may be medical equipment using abio-information database (DB) and/or an estimation result ofbio-information, a printer to print out results, or a display to displaythe estimation result of bio-information. In addition, the externaldevice may be a digital TV, a desktop computer, a cellular phone, asmartphone, a tablet PC, a laptop computer, a personal digital assistant(PDA), a portable multimedia player (PMP), a navigation, an MP3 player,a digital camera, a wearable device, and the like, but the externaldevice is not limited thereto.

The communication interface 750 may communicate with an external deviceby using Bluetooth communication, Bluetooth Low Energy (BLE)communication, Near Field Communication (NFC), WLAN communication,Zigbee communication, Infrared Data Association (IrDA) communication,Wi-Fi Direct (WFD) communication, Ultra-Wideband (UWB) communication,Ant+ communication, WIFI communication, Radio Frequency Identification(RFID) communication, 3G communication, 4G communication, 5Gcommunication, and the like. However, this is merely exemplary and isnot intended to be limiting.

The output interface 760 may output at least one of a bio-signal, afeature value of a bio-signal, a multiplication coefficient ratiocontrol factor, a multiplication coefficient ratio, and estimatedbio-information.

For example, the output interface 760 may output at least one or more ofthe estimation result of bio-information, warning information on a stateof the acquired bio-signal, and reliability of the estimatedbio-information by using at least one of an acoustic method, a visualmethod, and a tactile method. To this end, the output interface 760 mayinclude a display, a speaker, a vibrator, and the like.

For example, the processor 720 may measure a bio-signal in a referencestate, and may output bio-signal measurement guide information forcalculating the reference first feature value, the reference secondfeature value, the reference third feature value, and an offset valuefor bio-information.

In addition, the processor 720 may receive a new bio-signal from anexternal bio-signal database (DB) through the communication interface750.

FIG. 8 is a flowchart illustrating an example of a method of estimatingbio-information. The bio-information estimating method of FIG. 8 may beperformed by the bio-information estimating apparatuses 100 and 700illustrated in FIGS. 1 and 7.

The bio-information estimating apparatus 700 may acquire a bio-signal inoperation 810.

The bio-information estimating apparatus 700 may include a sensorincluding at least one of the following: one or more electrodes formeasuring a bio-signal, a PPG sensor, an ECG sensor, a pressure sensor,and a photodetector module including a light source and a detector. Thebio-information estimating apparatus 700 may directly interface with auser to acquire a bio-signal. Further, the bio-information estimatingapparatus 700 is not limited thereto, and may communicate with anexternal device to receive bio-signal data of a user from the externaldevice.

Upon acquiring the bio-signal, the bio-information estimating apparatus700 may extract one or more first feature values f₁ from the acquiredbio-signal in operation 820.

The first feature values, which are extracted from the bio-signal, mayindicate features having a predetermined correlation withbio-information desired to be estimated, and may vary depending on thetypes of bio-information.

Upon extracting the first feature values from the bio-signal, thebio-information estimating apparatus 700 may convert the first featurevalues. For example, the bio-information estimating apparatus 700 maynormalize the first feature values by dividing the extracted firstfeature value by a reference first feature value extracted in areference state.

In this case, the reference state is a resting state except for a sleepstate, and may refer to, for example, a state in which pulse andrespiration rates are stable, or a state in which blood pressuremeasured by an external device for measuring blood pressure ismaintained without substantial change. The reference state may bemeasured from a test subject while the test subject is awake and restingwithout exercising.

Further, a reference second feature value and a reference third value,which will be described later, may refer to a second feature value and athird feature value which are calculated based on the reference firstfeature value extracted in the reference state.

Then, the bio-information estimating apparatus 700 may determine a scalefactor based on the extracted first feature values in operation 830.Operation 830 may include a first step of calculating a scale controlfactor, and a second step of multiplying a default scale factor by thescale control ratio. For example, the bio-information estimatingapparatus 700 may store the default scale factor in a memory, and mayadjust the default scale factor by multiplying the default scale factorand the scale control ratio. The term “default scale factor” may be alsoreferred to as “reference scale factor.”

Specifically, the bio-information estimating apparatus 700 may calculatea second feature value f_(f) by combining the first feature values, andmay calculate a scale control ratio based on the second feature value.

For example, upon calculating one or more first feature values, thebio-information estimating apparatus 700 may calculate the secondfeature value by using an individual variation and/or a combinedvariation of the first feature values.

In this case, the combined variation may refer to a difference between avalue, obtained by linear combination of the first feature values f_(1a)and f_(1b) (e.g., addition, subtraction, and multiplication of f_(1a)and f_(1b), a combination thereof, etc.), and the reference secondfeature value f_(sc, ref); and the individual variation may refer to adifference between each of the first feature values f_(1a) and f_(1b)and the reference second feature value f_(sc, ref).

The bio-information estimating apparatus 700 may apply a weight to afirst feature value, having a higher correlation with bio-information tobe estimated than other first feature values among the extracted firstfeature values.

As described above, upon calculating the second feature value, thebio-information estimating apparatus 700 may calculate a scale controlratio based on the second feature value, and may determine a scalefactor by adjusting a reference scale factor based on the calculatedscale control ratio.

For example, the bio-information estimating apparatus 700 may calculatea scale control ratio according to a magnitude of the second featurevalue by applying the calculated second feature value to a scale controlratio decision function.

Here, a scale control ratio decision function may have a valley shape,in which the scale control ratio has a minimum value ρ_(min) at a pointof the reference second feature value f_(sc_ref), and increases with thechange in the second feature value from the reference second featurevalue, and in the case where the second feature value falls outside athreshold range, the scale control ratio is saturated to a predeterminedscale control ratio

Here, the threshold range may indicate an interval between a low pointμ_(low) and a high point μ_(high) of the second feature value. In otherwords, the threshold range may indicate a region where the first featurevalue is changed from the homoeostasis maintaining region, and a regionwhere the scale control ratio changes adaptively. That is, in the casewhere the second feature value f_(sc) changes within the predeterminedthreshold range, the scale control ratio increases in both directionsfrom the reference second feature value.

As described above, since the scale control ratio has a minimum value ata point of the reference second feature value, the change of bio-signalfeatures in a stable state, such as a reference state, may have a smalleffect on estimation of bio-information; and as the second feature valuedeviates from the reference state, the scale control ratio forestimating bio-information increases. Accordingly, as an amplitude of abio-signal increases, the change of the bio-signal features may have agreater effect on estimation of bio-information.

Then, as the second feature value continuously changes to fall outsidethe threshold range, the scale control ratio is saturated to apredetermined scale control ratio (e.g., 1), such that thebio-information estimating apparatus 700 may determine the predeterminedreference scale factor itself to be a scale factor.

In another example, the bio-information estimating apparatus 700 maycalculate an individual scale control ratio for each of the firstfeature values based on the scale control ratio decision function foreach of the first feature values.

For example, upon extracting first feature values f_(1a), f_(1b), andf_(1c), instead of generating a second feature value by combining thefirst feature values, the bio-information estimating apparatus 700 maycalculate individual scale control ratios ρ₁, ρ₂, and ρ₃ using scalecontrol ratio functions ρ₁(f_(1a)), ρ₂(f_(1b)), and ρ₃(f_(1c)) for eachof the first feature values, and may use a statistical value of theindividual scale control ratios as a scale control ratio.

As described above, upon calculating the scale control ratio, thebio-information estimating apparatus 700 may determine a scale factor byadjusting a reference scale factor using the calculated scale controlratio.

Then, upon determining the scale factor, the bio-information estimatingapparatus 700 may estimate bio-information based on the determined scalefactor and the first feature values in operation 840. Operation 840 mayinclude a first step of subtracting a reference feature f_(est_ref) froman extracted feature f_(est) of the bio-signal to obtain a first value,a second step of multiplying the first value by the adjusted scalefactor SF to obtain a second value, and a third step of adding an offsetvalue BI_(offset) to the second value to obtain an estimated resultBI_(est).

For example, the bio-information estimating apparatus 700 may calculatea third feature value f_(est) by combining the first feature values, andmay estimate bio-information based on the calculated third feature valueand the determined scale factor.

Upon calculating the third feature value, the bio-information estimatingapparatus 700 may estimate bio-information by multiplying a differencebetween the third feature value and the reference third feature value bythe determined scale factor, and by adding the offset value forbio-information to the multiplied value.

FIG. 9 is a flowchart illustrating another example of a method ofestimating bio-information.

Acquiring of a bio-signal in operation 910, extracting one or more firstfeature values from the acquired bio-signal in operation 920,determining a scale factor based on the extracted first feature valuesin operation 950, and estimating bio-information based on the determinedscale factor and the first feature values in operation 960 aresubstantially the same as the acquiring of the bio-signal in operation810, the extracting of the one or more first feature values from theacquired bio-signal in operation 820, the determining of the scalefactor based on the extracted first feature values in operation 830, andthe estimating of the bio-information based on the determined scalefactor and the first feature values in operation 840, such that thefollowing description will be made based on non-overlapping parts.

The bio-information estimating apparatus 700 may acquire a bio-signal inoperation 910.

Upon acquiring the bio-signal, the bio-information estimating apparatus700 may extract one or more first feature values from the acquiredbio-signal in operation 920.

Then, the bio-information estimating apparatus 700 may determine whetherthe first feature value exceeds a predetermined threshold value inoperation 930.

For example, the bio-information estimating apparatus 700 may comparethe extracted first feature value with a value at a predetermined lowpoint μ_(low) or a predetermined high point ρ_(high) of the scalecontrol ratio decision function, and may determine whether the firstfeature value is lower than the predetermined low point μ_(low) orhigher than the predetermined high point μ_(high).

Upon determining that the first feature value exceeds the thresholdvalue, the bio-information estimating apparatus 700 may determine thereference scale factor to be a scale factor in operation 940.

For example, the bio-information estimating apparatus 700 may comparethe first feature value with a predetermined threshold value; and inresponse to the first feature value exceeding the threshold value basedon the comparison, the bio-information estimating apparatus 700 maydetermine that a variation in the first feature value deviates from thehomoeostasis maintaining region and enters the linear change region, andmay determine the reference scale factor to be a scale control factor.

As described above, the bio-information estimating apparatus 700 maydetermine whether the first feature value exceeds a predeterminedthreshold value; and in response to the first feature value exceedingthe threshold value, the bio-information estimating apparatus 700 mayomit calculation of the scale control ratio, thereby accurately andrapidly estimating bio-information.

Further, in response to the first feature value being lower than thepredetermined threshold value, the bio-information estimating apparatus700 may determine that a variation of the bio-signal belongs to thehomoeostasis maintaining region, and may determine a scale factor basedon the extracted first feature value in operation 950.

Upon determining the scale factor, the bio-information estimatingapparatus 700 may estimate bio-information based on the determined scalefactor and the first feature values in operation 960.

While not restricted thereto, an example embodiment can be embodied ascomputer-readable code on a computer-readable recording medium. Thecomputer-readable recording medium is any data storage device that canstore data that can be thereafter read by a computer system. Examples ofthe computer-readable recording medium include read-only memory (ROM),random-access memory (RAM), CD-ROMs, magnetic tapes, floppy disks, andoptical data storage devices. The computer-readable recording medium canalso be distributed over network-coupled computer systems so that thecomputer-readable code is stored and executed in a distributed fashion.Also, an example embodiment may be written as a computer programtransmitted over a computer-readable transmission medium, such as acarrier wave, and received and implemented in general-use orspecial-purpose digital computers that execute the programs. Moreover,it is understood that in example embodiments, one or more units of theabove-described apparatuses and devices can include circuitry, aprocessor, a microprocessor, etc., and may execute a computer programstored in a computer-readable medium.

The foregoing example embodiments are merely example and are not to beconstrued as limiting. The present teaching can be readily applied toother types of apparatuses. Also, the description of the exampleembodiments is intended to be illustrative, and not to limit the scopeof the claims, and many alternatives, modifications, and variations willbe apparent to those skilled in the art.

What is claimed is:
 1. An apparatus for estimating bio-information, theapparatus comprising: a bio-signal acquirer configured to acquire abio-signal; and a processor configured to extract one or more firstfeature values from the bio-signal, determine a scale factor based onthe first feature values, and estimate bio-information based on thescale factor and the first feature values.
 2. The apparatus of claim 1,wherein the first features comprise a feature associated with cardiacoutput (CO), a feature associated with total peripheral resistance(TPR), and a combination of the feature associated with CO and thefeature associated with the TPR.
 3. The apparatus of claim 1, whereinthe processor is further configured to calculate a second feature valueby combining the first feature values, calculate a scale control ratiobased on the second feature value, and adjust a reference scale factorbased on the scale control ratio to determine the scale factor.
 4. Theapparatus of claim 3, wherein the processor is further configured tocalculate the second feature value by combining at least one of anindividual variation and a combined variation of the first featurevalues.
 5. The apparatus of claim 3, wherein the processor is furtherconfigured to calculate the scale control ratio according to a magnitudeof the second feature value by applying the second feature value to ascale control ratio decision function.
 6. The apparatus of claim 5,wherein the scale control ratio decision function is expressed as agraph having a valley shape, in which the scale control ratio has aminimum value at a point of a reference second feature value andincreases with a change in the second feature value from the referencesecond feature value, and the scale control ratio is saturated to apredetermined scale control ratio in an area of the graph where thesecond feature value falls outside a threshold range.
 7. The apparatusof claim 1, wherein the processor is further configured to calculateindividual scale control ratios for the first feature values, calculatea scale control ratio based on a statistical value of the individualscale control ratios, and determine the scale factor based on the scalecontrol ratio.
 8. The apparatus of claim 1, wherein the processor isfurther configured to calculate a third feature value by combining thefirst feature values, and estimate bio-information based on the thirdfeature value and the scale factor.
 9. The apparatus of claim 8, whereinthe processor is further configured to estimate the bio-information bymultiplying a difference between the third feature value and a referencethird feature value by the scale factor, and adding an offset value tothe multiplied difference.
 10. The apparatus of claim 1, wherein inresponse to the first feature value exceeding a predetermined thresholdvalue, the processor is further configured to determine a referencescale factor to be the scale factor.
 11. The apparatus of claim 1,wherein the processor is further configured to normalize the firstfeature values based on a reference first feature value.
 12. Theapparatus of claim 1, wherein the bio-information comprises at least oneof blood pressure, cardiac output, blood glucose, triglycerides, andkeratin.
 13. A method of estimating bio-information, the methodcomprising: acquiring a bio-signal; extracting one or more first featurevalues from the bio-signal; determining a scale factor based on thefirst feature values; and estimating bio-information based on the scalefactor and the first feature values.
 14. The method of claim 13, whereinthe determining the scale factor comprises: calculating a second featurevalue by combining the first feature values; calculating a scale controlratio based on the second feature value; and determining the scalefactor by adjusting a reference scale factor based on the scale controlratio.
 15. The method of claim 14, wherein the calculating the secondfeature value comprises combining at least one of an individualvariation and a combined variation of the first feature values.
 16. Themethod of claim 14, wherein the calculating the scale control ratiocomprises calculating the scale control ratio according to a magnitudeof the second feature value by applying the second feature value to ascale control ratio decision function.
 17. The method of claim 16,wherein the scale control ratio decision function is expressed as agraph having a valley shape, in which the scale control ratio has aminimum value at a point of a reference second feature value andincreases with a change in the second feature value from the referencesecond feature value, and the scale control ratio is saturated to apredetermined scale control ratio in an area of the graph where thesecond feature value falls outside a threshold range.
 18. The method ofclaim 13, wherein the determining the scale factor comprises:calculating individual scale control ratios for the first featurevalues; calculating a scale control ratio based on a statistical valueof the individual scale control ratios; and determining the scale factorby adjusting the reference scale factor based on the calculated scalecontrol ratio.
 19. The method of claim 13, wherein the estimating thebio-information comprises: calculating a third feature value bycombining the first feature values; multiplying a difference between thethird feature value and a reference third feature value by the scalefactor; and adding an offset value to the multiplied difference toestimate the bio-information.
 20. The method of claim 13, wherein thedetermining the scale factor comprises: determining whether the firstfeature value exceeds a predetermined threshold value; and upondetermining that the first feature value exceeds the predeterminedthreshold value, determining a reference scale factor to be the scalefactor.